Author name cluster

Yuning Jiang

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7 papers

1 author row

AAAI Conference 2026 Conference Paper

MIRAGE: Towards AI-Generated Image Detection in the Wild

OuCheng Huang
Manxi Lin
Jiexiang Tan
Xiaoxiong Du
Yang Qiu
Junjun Zheng
Xiangheng Kong
Yuning Jiang

The spreading of AI-generated images (AIGI), driven by advances in generative AI, poses a significant threat to in- formation security and public trust. Existing AIGI detectors, while effective against images in clean laboratory settings, fail to generalize to in-the-wild scenarios. These real-world images are noisy, varying from “obviously fake” images to realistic ones derived from multiple generative models and further edited for quality control. We address in-the-wild AIGI detection in this paper. We introduce MIRAGE, a challenging benchmark designed to emulate the complexity of in-the-wild AIGI. MIRAGE is constructed from two sources: (1) a large corpus of Internet-sourced AIGI verified by human experts, and (2) a synthesized dataset created through the collaboration between multiple expert generators, closely simulating the realistic AIGI in the wild. Building on this benchmark, we propose MIRAGE-R1, a vision- language model with heuristic-to-analytic reasoning, a reflective reasoning mechanism for AIGI detection. MIRAGE-R1 is trained in two stages: a supervised-fine-tuning cold start, followed by a reinforcement learning stage. By further adopting a inference-time adaptive thinking strategy, MIRAGE-R1 is able to provide either a quick judgment or a more robust and accurate conclusion, effectively balancing inference speed and performance. Extensive experiments show that our model leads state-of-the-art detectors by 5% and 10% on MIRAGE and public benchmark, respectively.

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NeurIPS Conference 2025 Conference Paper

TranSUN: A Preemptive Paradigm to Eradicate Retransformation Bias Intrinsically from Regression Models in Recommender Systems

Jiahao Yu
Haozhuang Liu
Yeqiu Yang
Lu Chen
Jian Wu
Yuning Jiang
Bo Zheng

Regression models are crucial in recommender systems. However, retransformation bias problem has been conspicuously neglected within the community. While many works in other fields have devised effective bias correction methods, all of them are post-hoc cures externally to the model, facing practical challenges when applied to real-world recommender systems. Hence, we propose a preemptive paradigm to eradicate the bias intrinsically from the models via minor model refinement. Specifically, a novel TranSUN method is proposed with a joint bias learning manner to offer theoretically guaranteed unbiasedness under empirical superior convergence. It is further generalized into a novel generic regression model family, termed Generalized TranSUN (GTS), which not only offers more theoretical insights but also serves as a generic framework for flexibly developing various bias-free models. Comprehensive experimental results demonstrate the superiority of our methods across data from various domains, which have been successfully deployed in two real-world industrial recommendation scenarios, i. e. product and short video recommendation scenarios in Guess What You Like business domain in the homepage of Taobao App (a leading e-commerce platform with DAU > 300M), to serve the major online traffic.

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AAAI Conference 2023 Conference Paper

Video Object of Interest Segmentation

Siyuan Zhou
Chunru Zhan
Biao Wang
Tiezheng Ge
Yuning Jiang
Li Niu

In this work, we present a new computer vision task named video object of interest segmentation (VOIS). Given a video and a target image of interest, our objective is to simultaneously segment and track all objects in the video that are relevant to the target image. This problem combines the traditional video object segmentation task with an additional image indicating the content that users are concerned with. Since no existing dataset is perfectly suitable for this new task, we specifically construct a large-scale dataset called LiveVideos, which contains 2418 pairs of target images and live videos with instance-level annotations. In addition, we propose a transformer-based method for this task. We revisit Swin Transformer and design a dual-path structure to fuse video and image features. Then, a transformer decoder is employed to generate object proposals for segmentation and tracking from the fused features. Extensive experiments on LiveVideos dataset show the superiority of our proposed method.

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IJCAI Conference 2022 Conference Paper

Composition-aware Graphic Layout GAN for Visual-Textual Presentation Designs

Min Zhou
Chenchen Xu
Ye Ma
Tiezheng Ge
Yuning Jiang
Weiwei Xu

In this paper, we study the graphic layout generation problem of producing high-quality visual-textual presentation designs for given images. We note that image compositions, which contain not only global semantics but also spatial information, would largely affect layout results. Hence, we propose a deep generative model, dubbed as composition-aware graphic layout GAN (CGL-GAN), to synthesize layouts based on the global and spatial visual contents of input images. To obtain training images from images that already contain manually designed graphic layout data, previous work suggests masking design elements (e. g. , texts and embellishments) as model inputs, which inevitably leaves hint of the ground truth. We study the misalignment between the training inputs (with hint masks) and test inputs (without masks), and design a novel domain alignment module (DAM) to narrow this gap. For training, we built a large-scale layout dataset which consists of 60, 548 advertising posters with annotated layout information. To evaluate the generated layouts, we propose three novel metrics according to aesthetic intuitions. Through both quantitative and qualitative evaluations, we demonstrate that the proposed model can synthesize high-quality graphic layouts according to image compositions. The data and code will be available at https: //github. com/minzhouGithub/CGL-GAN.

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AAAI Conference 2020 Conference Paper

DeepDualMapper: A Gated Fusion Network for Automatic Map Extraction Using Aerial Images and Trajectories

Hao Wu
Hanyuan Zhang
Xinyu Zhang
Weiwei Sun
Baihua Zheng
Yuning Jiang

Automatic map extraction is of great importance to urban computing and location-based services. Aerial image and GPS trajectory data refer to two different data sources that could be leveraged to generate the map, although they carry different types of information. Most previous works on data fusion between aerial images and data from auxiliary sensors do not fully utilize the information of both modalities and hence suffer from the issue of information loss. We propose a deep convolutional neural network called DeepDualMapper which fuses the aerial image and trajectory data in a more seamless manner to extract the digital map. We design a gated fusion module to explicitly control the information ﬂows from both modalities in a complementary-aware manner. Moreover, we propose a novel densely supervised reﬁnement decoder to generate the prediction in a coarse-to-ﬁne way. Our comprehensive experiments demonstrate that DeepDualMapper can fuse the information of images and trajectories much more effectively than existing approaches, and is able to generate maps with higher accuracy.

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AAAI Conference 2020 Conference Paper

Task-Aware Monocular Depth Estimation for 3D Object Detection

Xinlong Wang
Wei Yin
Tao Kong
Yuning Jiang
Lei Li
Chunhua Shen

Monocular depth estimation enables 3D perception from a single 2D image, thus attracting much research attention for years. Almost all methods treat foreground and background regions (“things and stuff”) in an image equally. However, not all pixels are equal. Depth of foreground objects plays a crucial role in 3D object recognition and localization. To date how to boost the depth prediction accuracy of foreground objects is rarely discussed. In this paper, we ﬁrst analyze the data distributions and interaction of foreground and background, then propose the foreground-background separated monocular depth estimation (ForeSeE) method, to estimate the foreground and background depth using separate optimization objectives and decoders. Our method signiﬁcantly improves the depth estimation performance on foreground objects. Applying ForeSeE to 3D object detection, we achieve 7. 5 AP gains and set new state-of-the-art results among other monocular methods. Code will be available at: https: //github. com/WXinlong/ForeSeE.

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AAAI Conference 2015 Conference Paper

Learning Face Hallucination in the Wild

Erjin Zhou
Haoqiang Fan
Zhimin Cao
Yuning Jiang
Qi Yin

Face hallucination method is proposed to generate highresolution images from low-resolution ones for better visualization. However, conventional hallucination methods are often designed for controlled settings and cannot handle varying conditions of pose, resolution degree, and blur. In this paper, we present a new method of face hallucination, which can consistently improve the resolution of face images even with large appearance variations. Our method is based on a novel network architecture called Bi-channel Convolutional Neural Network (Bi-channel CNN). It extracts robust face representations from raw input by using deep convolutional network, then adaptively integrates two channels of information (the raw input image and face representations) to predict the high-resolution image. Experimental results show our system outperforms the prior stateof-the-art methods.

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